{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市气候与海洋的关系研究"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据各个海滨城市数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 11)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>19</td>\n",
       "      <td>20.56</td>\n",
       "      <td>93</td>\n",
       "      <td>1010</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437799260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-25 06:41:00</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>20</td>\n",
       "      <td>20.72</td>\n",
       "      <td>94</td>\n",
       "      <td>1009</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437802783</td>\n",
       "      <td>1.5</td>\n",
       "      <td>200</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-25 07:39:43</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>21</td>\n",
       "      <td>22.70</td>\n",
       "      <td>88</td>\n",
       "      <td>1009</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437806424</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-25 08:40:24</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>22</td>\n",
       "      <td>24.51</td>\n",
       "      <td>73</td>\n",
       "      <td>1009</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437809987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-25 09:39:47</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>23</td>\n",
       "      <td>26.37</td>\n",
       "      <td>65</td>\n",
       "      <td>1009</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1437813633</td>\n",
       "      <td>1.5</td>\n",
       "      <td>70</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-25 10:40:33</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure description          dt  wind_speed  \\\n",
       "61          19  20.56        93      1010  light rain  1437799260         1.0   \n",
       "62          20  20.72        94      1009  light rain  1437802783         1.5   \n",
       "63          21  22.70        88      1009  light rain  1437806424         0.5   \n",
       "64          22  24.51        73      1009  light rain  1437809987         1.0   \n",
       "65          23  26.37        65      1009  few clouds  1437813633         1.5   \n",
       "\n",
       "    wind_deg    city                  day  dist  \n",
       "61         0  Milano  2015-07-25 06:41:00   250  \n",
       "62       200  Milano  2015-07-25 07:39:43   250  \n",
       "63         0  Milano  2015-07-25 08:40:24   250  \n",
       "64         0  Milano  2015-07-25 09:39:47   250  \n",
       "65        70  Milano  2015-07-25 10:40:33   250  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "milano1 = pd.read_csv('milano_270615.csv')\n",
    "milano2 = pd.read_csv('milano_150715.csv')\n",
    "milano3 = pd.read_csv('milano_250715.csv')\n",
    "# milano3\n",
    "# 合并数据\n",
    "milano = pd.concat([milano1, milano2, milano3], ignore_index=True)\n",
    "# display(milano.shape, milano.tail())\n",
    "\n",
    "# 将DataFrame对象，以csv文件形式保存起来\n",
    "# milano.to_csv('milano.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>28.05</td>\n",
       "      <td>66</td>\n",
       "      <td>1014</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863176</td>\n",
       "      <td>2.57</td>\n",
       "      <td>42.501</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 10:39:36</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.51</td>\n",
       "      <td>64</td>\n",
       "      <td>1014</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866759</td>\n",
       "      <td>1.54</td>\n",
       "      <td>263.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 11:39:19</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.39</td>\n",
       "      <td>58</td>\n",
       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870510</td>\n",
       "      <td>2.60</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 12:41:50</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>31.10</td>\n",
       "      <td>54</td>\n",
       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874098</td>\n",
       "      <td>2.10</td>\n",
       "      <td>90.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 13:41:38</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>33.23</td>\n",
       "      <td>45</td>\n",
       "      <td>1016</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436877645</td>\n",
       "      <td>2.10</td>\n",
       "      <td>120.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 14:40:45</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19</td>\n",
       "      <td>21.48</td>\n",
       "      <td>77</td>\n",
       "      <td>1009</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "      <td>1437799261</td>\n",
       "      <td>1.50</td>\n",
       "      <td>330.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-25 06:41:01</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20</td>\n",
       "      <td>20.97</td>\n",
       "      <td>93</td>\n",
       "      <td>1007</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437802783</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-25 07:39:43</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21</td>\n",
       "      <td>22.74</td>\n",
       "      <td>78</td>\n",
       "      <td>1010</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437806425</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-25 08:40:25</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22</td>\n",
       "      <td>24.34</td>\n",
       "      <td>64</td>\n",
       "      <td>1010</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437809987</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-25 09:39:47</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
       "      <td>25.58</td>\n",
       "      <td>77</td>\n",
       "      <td>1006</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437813633</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-25 10:40:33</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure           description          dt  \\\n",
       "0            0  28.05        66      1014          Sky is Clear  1436863176   \n",
       "1            1  29.51        64      1014          Sky is Clear  1436866759   \n",
       "2            2  30.39        58      1017          Sky is Clear  1436870510   \n",
       "3            3  31.10        54      1017          Sky is Clear  1436874098   \n",
       "4            4  33.23        45      1016            few clouds  1436877645   \n",
       "..         ...    ...       ...       ...                   ...         ...   \n",
       "63          19  21.48        77      1009  heavy intensity rain  1437799261   \n",
       "64          20  20.97        93      1007         moderate rain  1437802783   \n",
       "65          21  22.74        78      1010          Sky is Clear  1437806425   \n",
       "66          22  24.34        64      1010          Sky is Clear  1437809987   \n",
       "67          23  25.58        77      1006            light rain  1437813633   \n",
       "\n",
       "    wind_speed  wind_deg  city                  day  dist  \n",
       "0         2.57    42.501  Asti  2015-07-14 10:39:36   315  \n",
       "1         1.54   263.000  Asti  2015-07-14 11:39:19   315  \n",
       "2         2.60   100.000  Asti  2015-07-14 12:41:50   315  \n",
       "3         2.10    90.000  Asti  2015-07-14 13:41:38   315  \n",
       "4         2.10   120.000  Asti  2015-07-14 14:40:45   315  \n",
       "..         ...       ...   ...                  ...   ...  \n",
       "63        1.50   330.000  Asti  2015-07-25 06:41:01   315  \n",
       "64        0.51     0.000  Asti  2015-07-25 07:39:43   315  \n",
       "65        0.50     0.000  Asti  2015-07-25 08:40:25   315  \n",
       "66        1.00     0.000  Asti  2015-07-25 09:39:47   315  \n",
       "67        2.06     0.000  Asti  2015-07-25 10:40:33   315  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 城市数据\n",
    "asti1 = pd.read_csv('asti_150715.csv')\n",
    "asti2 = pd.read_csv('asti_270615.csv')\n",
    "asti3 = pd.read_csv('asti_250715.csv')\n",
    "\n",
    "asti = pd.concat([asti1, asti2, asti3], ignore_index=True)\n",
    "# asti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>29.98</td>\n",
       "      <td>57</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863101</td>\n",
       "      <td>0.51</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 10:38:21</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>30.26</td>\n",
       "      <td>51</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436866691</td>\n",
       "      <td>1.03</td>\n",
       "      <td>157.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 11:38:11</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>32.36</td>\n",
       "      <td>46</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>1436870392</td>\n",
       "      <td>2.06</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 12:39:52</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>31.16</td>\n",
       "      <td>47</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436874000</td>\n",
       "      <td>2.06</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 13:40:00</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>33.48</td>\n",
       "      <td>44</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>1436877549</td>\n",
       "      <td>2.06</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 14:39:09</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19</td>\n",
       "      <td>25.25</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437799170</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 06:39:30</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20</td>\n",
       "      <td>25.96</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437802712</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 07:38:32</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21</td>\n",
       "      <td>27.05</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437806349</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 08:39:09</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22</td>\n",
       "      <td>29.32</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1437809914</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 09:38:34</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
       "      <td>30.78</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437813555</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 10:39:15</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure    description          dt  \\\n",
       "0            0  29.98        57    1021.0   Sky is Clear  1436863101   \n",
       "1            1  30.26        51    1021.0  moderate rain  1436866691   \n",
       "2            2  32.36        46    1021.0   sky is clear  1436870392   \n",
       "3            3  31.16        47    1021.0  moderate rain  1436874000   \n",
       "4            4  33.48        44    1021.0   sky is clear  1436877549   \n",
       "..         ...    ...       ...       ...            ...         ...   \n",
       "63          19  25.25        61    1006.0   Sky is Clear  1437799170   \n",
       "64          20  25.96        61    1006.0   Sky is Clear  1437802712   \n",
       "65          21  27.05        61    1006.0  moderate rain  1437806349   \n",
       "66          22  29.32        61    1006.0     few clouds  1437809914   \n",
       "67          23  30.78        61    1006.0  moderate rain  1437813555   \n",
       "\n",
       "    wind_speed  wind_deg     city                  day  dist  \n",
       "0         0.51      90.0  Bologna  2015-07-14 10:38:21    71  \n",
       "1         1.03     157.0  Bologna  2015-07-14 11:38:11    71  \n",
       "2         2.06      67.0  Bologna  2015-07-14 12:39:52    71  \n",
       "3         2.06      90.0  Bologna  2015-07-14 13:40:00    71  \n",
       "4         2.06     135.0  Bologna  2015-07-14 14:39:09    71  \n",
       "..         ...       ...      ...                  ...   ...  \n",
       "63        4.11     171.0  Bologna  2015-07-25 06:39:30    71  \n",
       "64        4.11     171.0  Bologna  2015-07-25 07:38:32    71  \n",
       "65        4.11     171.0  Bologna  2015-07-25 08:39:09    71  \n",
       "66        4.11     171.0  Bologna  2015-07-25 09:38:34    71  \n",
       "67        4.11     171.0  Bologna  2015-07-25 10:39:15    71  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bologna1 = pd.read_csv('bologna_150715.csv')\n",
    "bologna2 = pd.read_csv('bologna_270615.csv')\n",
    "bologna3 = pd.read_csv('bologna_250715.csv')\n",
    "bologna = pd.concat([bologna1, bologna2, bologna3], ignore_index=True)\n",
    "# bologna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
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       "      <td>29.98</td>\n",
       "      <td>57</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863101</td>\n",
       "      <td>0.51</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 10:38:21</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>30.26</td>\n",
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       "      <td>1021.0</td>\n",
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       "      <td>1436866691</td>\n",
       "      <td>1.03</td>\n",
       "      <td>157.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 11:38:11</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>32.36</td>\n",
       "      <td>46</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>1436870392</td>\n",
       "      <td>2.06</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 12:39:52</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>31.16</td>\n",
       "      <td>47</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436874000</td>\n",
       "      <td>2.06</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 13:40:00</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>33.48</td>\n",
       "      <td>44</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>1436877549</td>\n",
       "      <td>2.06</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 14:39:09</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19</td>\n",
       "      <td>25.25</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437799170</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 06:39:30</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20</td>\n",
       "      <td>25.96</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437802712</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 07:38:32</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21</td>\n",
       "      <td>27.05</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437806349</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 08:39:09</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22</td>\n",
       "      <td>29.32</td>\n",
       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1437809914</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 09:38:34</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
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       "      <td>61</td>\n",
       "      <td>1006.0</td>\n",
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       "      <td>1437813555</td>\n",
       "      <td>4.11</td>\n",
       "      <td>171.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-25 10:39:15</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure    description          dt  \\\n",
       "0            0  29.98        57    1021.0   Sky is Clear  1436863101   \n",
       "1            1  30.26        51    1021.0  moderate rain  1436866691   \n",
       "2            2  32.36        46    1021.0   sky is clear  1436870392   \n",
       "3            3  31.16        47    1021.0  moderate rain  1436874000   \n",
       "4            4  33.48        44    1021.0   sky is clear  1436877549   \n",
       "..         ...    ...       ...       ...            ...         ...   \n",
       "63          19  25.25        61    1006.0   Sky is Clear  1437799170   \n",
       "64          20  25.96        61    1006.0   Sky is Clear  1437802712   \n",
       "65          21  27.05        61    1006.0  moderate rain  1437806349   \n",
       "66          22  29.32        61    1006.0     few clouds  1437809914   \n",
       "67          23  30.78        61    1006.0  moderate rain  1437813555   \n",
       "\n",
       "    wind_speed  wind_deg     city                  day  dist  \n",
       "0         0.51      90.0  Bologna  2015-07-14 10:38:21    71  \n",
       "1         1.03     157.0  Bologna  2015-07-14 11:38:11    71  \n",
       "2         2.06      67.0  Bologna  2015-07-14 12:39:52    71  \n",
       "3         2.06      90.0  Bologna  2015-07-14 13:40:00    71  \n",
       "4         2.06     135.0  Bologna  2015-07-14 14:39:09    71  \n",
       "..         ...       ...      ...                  ...   ...  \n",
       "63        4.11     171.0  Bologna  2015-07-25 06:39:30    71  \n",
       "64        4.11     171.0  Bologna  2015-07-25 07:38:32    71  \n",
       "65        4.11     171.0  Bologna  2015-07-25 08:39:09    71  \n",
       "66        4.11     171.0  Bologna  2015-07-25 09:38:34    71  \n",
       "67        4.11     171.0  Bologna  2015-07-25 10:39:15    71  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ferrara1 = pd.read_csv('ferrara_150715.csv')\n",
    "ferrara2 = pd.read_csv('ferrara_270615.csv')\n",
    "ferrara3 = pd.read_csv('ferrara_250715.csv')\n",
    "ferrara = pd.concat([bologna1, bologna2, bologna3], ignore_index=True)\n",
    "# ferrara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>28.34</td>\n",
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       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863109</td>\n",
       "      <td>3.1</td>\n",
       "      <td>20</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-14 10:38:29</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.25</td>\n",
       "      <td>65</td>\n",
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       "      <td>1436866696</td>\n",
       "      <td>3.1</td>\n",
       "      <td>80</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-14 11:38:16</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.40</td>\n",
       "      <td>58</td>\n",
       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870399</td>\n",
       "      <td>2.6</td>\n",
       "      <td>100</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-14 12:39:59</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>31.37</td>\n",
       "      <td>54</td>\n",
       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874005</td>\n",
       "      <td>2.1</td>\n",
       "      <td>90</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-14 13:40:05</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>32.59</td>\n",
       "      <td>45</td>\n",
       "      <td>1016</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436877558</td>\n",
       "      <td>2.1</td>\n",
       "      <td>120</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-14 14:39:18</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19</td>\n",
       "      <td>21.84</td>\n",
       "      <td>77</td>\n",
       "      <td>1009</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>1437799177</td>\n",
       "      <td>1.5</td>\n",
       "      <td>330</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-25 06:39:37</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20</td>\n",
       "      <td>21.50</td>\n",
       "      <td>88</td>\n",
       "      <td>1010</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>1437802719</td>\n",
       "      <td>1.5</td>\n",
       "      <td>50</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-25 07:38:39</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21</td>\n",
       "      <td>23.54</td>\n",
       "      <td>78</td>\n",
       "      <td>1010</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437806353</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-25 08:39:13</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22</td>\n",
       "      <td>25.72</td>\n",
       "      <td>64</td>\n",
       "      <td>1010</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437809920</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-25 09:38:40</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
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       "      <td>64</td>\n",
       "      <td>1010</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437813560</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>Torino</td>\n",
       "      <td>2015-07-25 10:39:20</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure    description          dt  \\\n",
       "0            0  28.34        65      1017   Sky is Clear  1436863109   \n",
       "1            1  29.25        65      1017   Sky is Clear  1436866696   \n",
       "2            2  30.40        58      1017   Sky is Clear  1436870399   \n",
       "3            3  31.37        54      1017   Sky is Clear  1436874005   \n",
       "4            4  32.59        45      1016     few clouds  1436877558   \n",
       "..         ...    ...       ...       ...            ...         ...   \n",
       "63          19  21.84        77      1009  broken clouds  1437799177   \n",
       "64          20  21.50        88      1010  broken clouds  1437802719   \n",
       "65          21  23.54        78      1010   Sky is Clear  1437806353   \n",
       "66          22  25.72        64      1010   Sky is Clear  1437809920   \n",
       "67          23  25.84        64      1010   Sky is Clear  1437813560   \n",
       "\n",
       "    wind_speed  wind_deg    city                  day  dist  \n",
       "0          3.1        20  Torino  2015-07-14 10:38:29   357  \n",
       "1          3.1        80  Torino  2015-07-14 11:38:16   357  \n",
       "2          2.6       100  Torino  2015-07-14 12:39:59   357  \n",
       "3          2.1        90  Torino  2015-07-14 13:40:05   357  \n",
       "4          2.1       120  Torino  2015-07-14 14:39:18   357  \n",
       "..         ...       ...     ...                  ...   ...  \n",
       "63         1.5       330  Torino  2015-07-25 06:39:37   357  \n",
       "64         1.5        50  Torino  2015-07-25 07:38:39   357  \n",
       "65         0.5         0  Torino  2015-07-25 08:39:13   357  \n",
       "66         1.0         0  Torino  2015-07-25 09:38:40   357  \n",
       "67         1.5         0  Torino  2015-07-25 10:39:20   357  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "torino1 = pd.read_csv('torino_150715.csv')\n",
    "torino2 = pd.read_csv('torino_270615.csv')\n",
    "torino3 = pd.read_csv('torino_250715.csv')\n",
    "torino = pd.concat([torino1, torino2, torino3], ignore_index=True)\n",
    "# torino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>28.66</td>\n",
       "      <td>51</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863113</td>\n",
       "      <td>2.1</td>\n",
       "      <td>140</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-14 10:38:33</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>30.10</td>\n",
       "      <td>45</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866700</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-14 11:38:20</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.14</td>\n",
       "      <td>42</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870406</td>\n",
       "      <td>2.1</td>\n",
       "      <td>170</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-14 12:40:06</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>30.74</td>\n",
       "      <td>45</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874012</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-14 13:40:12</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>31.22</td>\n",
       "      <td>38</td>\n",
       "      <td>1015</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436877561</td>\n",
       "      <td>1.5</td>\n",
       "      <td>180</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-14 14:39:21</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19</td>\n",
       "      <td>23.40</td>\n",
       "      <td>73</td>\n",
       "      <td>1008</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437799182</td>\n",
       "      <td>2.6</td>\n",
       "      <td>310</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-25 06:39:42</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20</td>\n",
       "      <td>24.73</td>\n",
       "      <td>61</td>\n",
       "      <td>1007</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437802723</td>\n",
       "      <td>3.1</td>\n",
       "      <td>330</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-25 07:38:43</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21</td>\n",
       "      <td>25.48</td>\n",
       "      <td>61</td>\n",
       "      <td>1008</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>1437806356</td>\n",
       "      <td>2.1</td>\n",
       "      <td>250</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-25 08:39:16</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22</td>\n",
       "      <td>26.94</td>\n",
       "      <td>54</td>\n",
       "      <td>1008</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>1437809924</td>\n",
       "      <td>3.1</td>\n",
       "      <td>280</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-25 09:38:44</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
       "      <td>27.80</td>\n",
       "      <td>51</td>\n",
       "      <td>1008</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1437813564</td>\n",
       "      <td>3.1</td>\n",
       "      <td>280</td>\n",
       "      <td>Mantova</td>\n",
       "      <td>2015-07-25 10:39:24</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure       description          dt  \\\n",
       "0            0  28.66        51      1016      Sky is Clear  1436863113   \n",
       "1            1  30.10        45      1016      Sky is Clear  1436866700   \n",
       "2            2  30.14        42      1016      Sky is Clear  1436870406   \n",
       "3            3  30.74        45      1016      Sky is Clear  1436874012   \n",
       "4            4  31.22        38      1015      Sky is Clear  1436877561   \n",
       "..         ...    ...       ...       ...               ...         ...   \n",
       "63          19  23.40        73      1008     moderate rain  1437799182   \n",
       "64          20  24.73        61      1007     moderate rain  1437802723   \n",
       "65          21  25.48        61      1008  scattered clouds  1437806356   \n",
       "66          22  26.94        54      1008  scattered clouds  1437809924   \n",
       "67          23  27.80        51      1008        few clouds  1437813564   \n",
       "\n",
       "    wind_speed  wind_deg     city                  day  dist  \n",
       "0          2.1       140  Mantova  2015-07-14 10:38:33   121  \n",
       "1          2.1         0  Mantova  2015-07-14 11:38:20   121  \n",
       "2          2.1       170  Mantova  2015-07-14 12:40:06   121  \n",
       "3          1.5         0  Mantova  2015-07-14 13:40:12   121  \n",
       "4          1.5       180  Mantova  2015-07-14 14:39:21   121  \n",
       "..         ...       ...      ...                  ...   ...  \n",
       "63         2.6       310  Mantova  2015-07-25 06:39:42   121  \n",
       "64         3.1       330  Mantova  2015-07-25 07:38:43   121  \n",
       "65         2.1       250  Mantova  2015-07-25 08:39:16   121  \n",
       "66         3.1       280  Mantova  2015-07-25 09:38:44   121  \n",
       "67         3.1       280  Mantova  2015-07-25 10:39:24   121  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mantova1 = pd.read_csv('mantova_150715.csv')\n",
    "mantova2 = pd.read_csv('mantova_270615.csv')\n",
    "mantova3 = pd.read_csv('mantova_250715.csv')\n",
    "mantova = pd.concat([mantova1, mantova2, mantova3], ignore_index=True)\n",
    "# mantova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
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       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>29.10</td>\n",
       "      <td>74</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436863177</td>\n",
       "      <td>3.10</td>\n",
       "      <td>10.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-14 10:39:37</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.51</td>\n",
       "      <td>74</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436866759</td>\n",
       "      <td>3.60</td>\n",
       "      <td>20.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-14 11:39:19</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>29.63</td>\n",
       "      <td>70</td>\n",
       "      <td>1016</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436870511</td>\n",
       "      <td>3.60</td>\n",
       "      <td>40.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-14 12:41:51</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>30.17</td>\n",
       "      <td>37</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436874106</td>\n",
       "      <td>4.63</td>\n",
       "      <td>90.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-14 13:41:46</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>30.45</td>\n",
       "      <td>34</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436877646</td>\n",
       "      <td>3.08</td>\n",
       "      <td>87.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-14 14:40:46</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>19</td>\n",
       "      <td>24.65</td>\n",
       "      <td>83</td>\n",
       "      <td>1007</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437799262</td>\n",
       "      <td>0.50</td>\n",
       "      <td>180.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-25 06:41:02</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>20</td>\n",
       "      <td>25.40</td>\n",
       "      <td>78</td>\n",
       "      <td>1007</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1437802784</td>\n",
       "      <td>0.50</td>\n",
       "      <td>190.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-25 07:39:44</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>21</td>\n",
       "      <td>27.23</td>\n",
       "      <td>54</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437806426</td>\n",
       "      <td>2.61</td>\n",
       "      <td>254.001</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-25 08:40:26</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>22</td>\n",
       "      <td>31.14</td>\n",
       "      <td>58</td>\n",
       "      <td>1008</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437809988</td>\n",
       "      <td>3.87</td>\n",
       "      <td>257.503</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-25 09:39:48</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>23</td>\n",
       "      <td>31.46</td>\n",
       "      <td>52</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437813634</td>\n",
       "      <td>1.00</td>\n",
       "      <td>190.000</td>\n",
       "      <td>Ravenna</td>\n",
       "      <td>2015-07-25 10:40:34</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure    description          dt  \\\n",
       "0            0  29.10        74      1015  moderate rain  1436863177   \n",
       "1            1  29.51        74      1015  moderate rain  1436866759   \n",
       "2            2  29.63        70      1016  moderate rain  1436870511   \n",
       "3            3  30.17        37      1015  moderate rain  1436874106   \n",
       "4            4  30.45        34      1015  moderate rain  1436877646   \n",
       "..         ...    ...       ...       ...            ...         ...   \n",
       "61          19  24.65        83      1007  moderate rain  1437799262   \n",
       "62          20  25.40        78      1007     few clouds  1437802784   \n",
       "63          21  27.23        54      1008   Sky is Clear  1437806426   \n",
       "64          22  31.14        58      1008  moderate rain  1437809988   \n",
       "65          23  31.46        52      1008   Sky is Clear  1437813634   \n",
       "\n",
       "    wind_speed  wind_deg     city                  day  dist  \n",
       "0         3.10    10.000  Ravenna  2015-07-14 10:39:37     8  \n",
       "1         3.60    20.000  Ravenna  2015-07-14 11:39:19     8  \n",
       "2         3.60    40.000  Ravenna  2015-07-14 12:41:51     8  \n",
       "3         4.63    90.000  Ravenna  2015-07-14 13:41:46     8  \n",
       "4         3.08    87.000  Ravenna  2015-07-14 14:40:46     8  \n",
       "..         ...       ...      ...                  ...   ...  \n",
       "61        0.50   180.000  Ravenna  2015-07-25 06:41:02     8  \n",
       "62        0.50   190.000  Ravenna  2015-07-25 07:39:44     8  \n",
       "63        2.61   254.001  Ravenna  2015-07-25 08:40:26     8  \n",
       "64        3.87   257.503  Ravenna  2015-07-25 09:39:48     8  \n",
       "65        1.00   190.000  Ravenna  2015-07-25 10:40:34     8  \n",
       "\n",
       "[66 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ravenna1 = pd.read_csv('ravenna_150715.csv')\n",
    "ravenna2 = pd.read_csv('ravenna_270615.csv')\n",
    "ravenna3 = pd.read_csv('ravenna_250715.csv')\n",
    "ravenna = pd.concat([ravenna1, ravenna2, ravenna3], ignore_index=True)\n",
    "# ravenna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>27.99</td>\n",
       "      <td>54</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863096</td>\n",
       "      <td>2.10</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-14 10:38:16</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.13</td>\n",
       "      <td>48</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866685</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-14 11:38:05</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.21</td>\n",
       "      <td>48</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870387</td>\n",
       "      <td>2.60</td>\n",
       "      <td>140.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-14 12:39:47</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>31.40</td>\n",
       "      <td>45</td>\n",
       "      <td>1015</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436873990</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-14 13:39:50</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>31.88</td>\n",
       "      <td>48</td>\n",
       "      <td>1010</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436877535</td>\n",
       "      <td>0.51</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-14 14:38:55</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19</td>\n",
       "      <td>21.88</td>\n",
       "      <td>100</td>\n",
       "      <td>1008</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437799162</td>\n",
       "      <td>2.10</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-25 06:39:22</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20</td>\n",
       "      <td>21.49</td>\n",
       "      <td>94</td>\n",
       "      <td>1009</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437802707</td>\n",
       "      <td>1.50</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-25 07:38:27</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21</td>\n",
       "      <td>22.54</td>\n",
       "      <td>88</td>\n",
       "      <td>1009</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437806342</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-25 08:39:02</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22</td>\n",
       "      <td>24.06</td>\n",
       "      <td>73</td>\n",
       "      <td>1009</td>\n",
       "      <td>light rain</td>\n",
       "      <td>1437809907</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-25 09:38:27</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
       "      <td>25.71</td>\n",
       "      <td>94</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437813549</td>\n",
       "      <td>3.10</td>\n",
       "      <td>340.0</td>\n",
       "      <td>Piacenza</td>\n",
       "      <td>2015-07-25 10:39:09</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure   description          dt  \\\n",
       "0            0  27.99        54      1016  Sky is Clear  1436863096   \n",
       "1            1  29.13        48      1016  Sky is Clear  1436866685   \n",
       "2            2  30.21        48      1016  Sky is Clear  1436870387   \n",
       "3            3  31.40        45      1015  Sky is Clear  1436873990   \n",
       "4            4  31.88        48      1010  Sky is Clear  1436877535   \n",
       "..         ...    ...       ...       ...           ...         ...   \n",
       "63          19  21.88       100      1008    light rain  1437799162   \n",
       "64          20  21.49        94      1009    light rain  1437802707   \n",
       "65          21  22.54        88      1009    light rain  1437806342   \n",
       "66          22  24.06        73      1009    light rain  1437809907   \n",
       "67          23  25.71        94      1008  Sky is Clear  1437813549   \n",
       "\n",
       "    wind_speed  wind_deg      city                  day  dist  \n",
       "0         2.10     100.0  Piacenza  2015-07-14 10:38:16   200  \n",
       "1         2.60       0.0  Piacenza  2015-07-14 11:38:05   200  \n",
       "2         2.60     140.0  Piacenza  2015-07-14 12:39:47   200  \n",
       "3         2.10       0.0  Piacenza  2015-07-14 13:39:50   200  \n",
       "4         0.51      23.0  Piacenza  2015-07-14 14:38:55   200  \n",
       "..         ...       ...       ...                  ...   ...  \n",
       "63        2.10     240.0  Piacenza  2015-07-25 06:39:22   200  \n",
       "64        1.50     200.0  Piacenza  2015-07-25 07:38:27   200  \n",
       "65        0.50       0.0  Piacenza  2015-07-25 08:39:02   200  \n",
       "66        1.00       0.0  Piacenza  2015-07-25 09:38:27   200  \n",
       "67        3.10     340.0  Piacenza  2015-07-25 10:39:09   200  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "piacenza1 = pd.read_csv('piacenza_150715.csv')\n",
    "piacenza2 = pd.read_csv('piacenza_270615.csv')\n",
    "piacenza3 = pd.read_csv('piacenza_250715.csv')\n",
    "piacenza = pd.concat([piacenza1, piacenza2, piacenza3], ignore_index=True)\n",
    "# piacenza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>29.15</td>\n",
       "      <td>83</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436863101</td>\n",
       "      <td>3.62</td>\n",
       "      <td>94.001</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-14 10:38:21</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.37</td>\n",
       "      <td>74</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436866691</td>\n",
       "      <td>3.60</td>\n",
       "      <td>20.000</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-14 11:38:11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>29.51</td>\n",
       "      <td>78</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436870392</td>\n",
       "      <td>3.60</td>\n",
       "      <td>70.000</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-14 12:39:52</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>29.88</td>\n",
       "      <td>70</td>\n",
       "      <td>1016</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436874000</td>\n",
       "      <td>4.60</td>\n",
       "      <td>60.000</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-14 13:40:00</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>30.12</td>\n",
       "      <td>70</td>\n",
       "      <td>1016</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436877549</td>\n",
       "      <td>4.10</td>\n",
       "      <td>70.000</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-14 14:39:09</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19</td>\n",
       "      <td>24.66</td>\n",
       "      <td>83</td>\n",
       "      <td>1007</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437799170</td>\n",
       "      <td>0.50</td>\n",
       "      <td>180.000</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-25 06:39:30</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20</td>\n",
       "      <td>25.33</td>\n",
       "      <td>94</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437802712</td>\n",
       "      <td>2.61</td>\n",
       "      <td>254.001</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-25 07:38:32</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>21</td>\n",
       "      <td>27.24</td>\n",
       "      <td>83</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437806348</td>\n",
       "      <td>2.61</td>\n",
       "      <td>254.001</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-25 08:39:08</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22</td>\n",
       "      <td>31.56</td>\n",
       "      <td>70</td>\n",
       "      <td>1008</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437809914</td>\n",
       "      <td>2.10</td>\n",
       "      <td>250.000</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-25 09:38:34</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
       "      <td>32.46</td>\n",
       "      <td>70</td>\n",
       "      <td>1008</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437813555</td>\n",
       "      <td>3.60</td>\n",
       "      <td>260.000</td>\n",
       "      <td>Cesena</td>\n",
       "      <td>2015-07-25 10:39:15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure    description          dt  \\\n",
       "0            0  29.15        83      1015  moderate rain  1436863101   \n",
       "1            1  29.37        74      1015  moderate rain  1436866691   \n",
       "2            2  29.51        78      1015  moderate rain  1436870392   \n",
       "3            3  29.88        70      1016  moderate rain  1436874000   \n",
       "4            4  30.12        70      1016  moderate rain  1436877549   \n",
       "..         ...    ...       ...       ...            ...         ...   \n",
       "63          19  24.66        83      1007  moderate rain  1437799170   \n",
       "64          20  25.33        94      1008   Sky is Clear  1437802712   \n",
       "65          21  27.24        83      1008   Sky is Clear  1437806348   \n",
       "66          22  31.56        70      1008  moderate rain  1437809914   \n",
       "67          23  32.46        70      1008  moderate rain  1437813555   \n",
       "\n",
       "    wind_speed  wind_deg    city                  day  dist  \n",
       "0         3.62    94.001  Cesena  2015-07-14 10:38:21    14  \n",
       "1         3.60    20.000  Cesena  2015-07-14 11:38:11    14  \n",
       "2         3.60    70.000  Cesena  2015-07-14 12:39:52    14  \n",
       "3         4.60    60.000  Cesena  2015-07-14 13:40:00    14  \n",
       "4         4.10    70.000  Cesena  2015-07-14 14:39:09    14  \n",
       "..         ...       ...     ...                  ...   ...  \n",
       "63        0.50   180.000  Cesena  2015-07-25 06:39:30    14  \n",
       "64        2.61   254.001  Cesena  2015-07-25 07:38:32    14  \n",
       "65        2.61   254.001  Cesena  2015-07-25 08:39:08    14  \n",
       "66        2.10   250.000  Cesena  2015-07-25 09:38:34    14  \n",
       "67        3.60   260.000  Cesena  2015-07-25 10:39:15    14  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cesena1 = pd.read_csv('cesena_150715.csv')\n",
    "cesena2 = pd.read_csv('cesena_270615.csv')\n",
    "cesena3 = pd.read_csv('cesena_250715.csv')\n",
    "cesena = pd.concat([cesena1, cesena2, cesena3], ignore_index=True)\n",
    "# cesena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>29.40</td>\n",
       "      <td>83</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436863177</td>\n",
       "      <td>3.62</td>\n",
       "      <td>94.001</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-14 10:39:37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>30.12</td>\n",
       "      <td>78</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436866759</td>\n",
       "      <td>3.10</td>\n",
       "      <td>80.000</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-14 11:39:19</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.10</td>\n",
       "      <td>78</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436870510</td>\n",
       "      <td>3.60</td>\n",
       "      <td>70.000</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-14 12:41:50</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>30.75</td>\n",
       "      <td>74</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436874099</td>\n",
       "      <td>4.60</td>\n",
       "      <td>90.000</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-14 13:41:39</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>30.71</td>\n",
       "      <td>66</td>\n",
       "      <td>1015</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436877646</td>\n",
       "      <td>5.10</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-14 14:40:46</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>19</td>\n",
       "      <td>24.77</td>\n",
       "      <td>94</td>\n",
       "      <td>1007</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437799262</td>\n",
       "      <td>1.00</td>\n",
       "      <td>230.000</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-25 06:41:02</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>20</td>\n",
       "      <td>25.59</td>\n",
       "      <td>94</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437802784</td>\n",
       "      <td>2.61</td>\n",
       "      <td>254.001</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-25 07:39:44</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>21</td>\n",
       "      <td>27.23</td>\n",
       "      <td>83</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437806426</td>\n",
       "      <td>2.61</td>\n",
       "      <td>254.001</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-25 08:40:26</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>22</td>\n",
       "      <td>30.20</td>\n",
       "      <td>70</td>\n",
       "      <td>1008</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1437809988</td>\n",
       "      <td>2.10</td>\n",
       "      <td>250.000</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-25 09:39:48</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>23</td>\n",
       "      <td>31.12</td>\n",
       "      <td>70</td>\n",
       "      <td>1008</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1437813634</td>\n",
       "      <td>3.60</td>\n",
       "      <td>260.000</td>\n",
       "      <td>Faenza</td>\n",
       "      <td>2015-07-25 10:40:34</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   temp  humidity  pressure    description          dt  \\\n",
       "0            0  29.40        83      1015  moderate rain  1436863177   \n",
       "1            1  30.12        78      1015  moderate rain  1436866759   \n",
       "2            2  30.10        78      1015  moderate rain  1436870510   \n",
       "3            3  30.75        74      1015  moderate rain  1436874099   \n",
       "4            4  30.71        66      1015  moderate rain  1436877646   \n",
       "..         ...    ...       ...       ...            ...         ...   \n",
       "62          19  24.77        94      1007  moderate rain  1437799262   \n",
       "63          20  25.59        94      1008   Sky is Clear  1437802784   \n",
       "64          21  27.23        83      1008   Sky is Clear  1437806426   \n",
       "65          22  30.20        70      1008   Sky is Clear  1437809988   \n",
       "66          23  31.12        70      1008  moderate rain  1437813634   \n",
       "\n",
       "    wind_speed  wind_deg    city                  day  dist  \n",
       "0         3.62    94.001  Faenza  2015-07-14 10:39:37    37  \n",
       "1         3.10    80.000  Faenza  2015-07-14 11:39:19    37  \n",
       "2         3.60    70.000  Faenza  2015-07-14 12:41:50    37  \n",
       "3         4.60    90.000  Faenza  2015-07-14 13:41:39    37  \n",
       "4         5.10   100.000  Faenza  2015-07-14 14:40:46    37  \n",
       "..         ...       ...     ...                  ...   ...  \n",
       "62        1.00   230.000  Faenza  2015-07-25 06:41:02    37  \n",
       "63        2.61   254.001  Faenza  2015-07-25 07:39:44    37  \n",
       "64        2.61   254.001  Faenza  2015-07-25 08:40:26    37  \n",
       "65        2.10   250.000  Faenza  2015-07-25 09:39:48    37  \n",
       "66        3.60   260.000  Faenza  2015-07-25 10:40:34    37  \n",
       "\n",
       "[67 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faenza1 = pd.read_csv('faenza_150715.csv')\n",
    "faenza2 = pd.read_csv('faenza_270615.csv')\n",
    "faenza3 = pd.read_csv('faenza_250715.csv')\n",
    "faenza = pd.concat([faenza1, faenza2, faenza3], ignore_index=True)\n",
    "# faenza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.read_csv('faenza_150715.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看行数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(66, 11)\n",
      "(68, 11)\n",
      "(67, 11)\n"
     ]
    }
   ],
   "source": [
    "print(ravenna.shape)\n",
    "print(cesena.shape)\n",
    "print(faenza.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- 去除没用的列 -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 去除没用的列,每个城市的都去除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Unnamed: 0'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-94-106e2036d103>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbologna\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unnamed: 0'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\hdr\\python\\python\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4310\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4311\u001b[0m             \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4312\u001b[1;33m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4313\u001b[0m         )\n\u001b[0;32m   4314\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hdr\\python\\python\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4150\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4151\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4152\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4154\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hdr\\python\\python\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   4185\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4186\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4187\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4188\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hdr\\python\\python\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5589\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5590\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5591\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5592\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5593\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['Unnamed: 0'] not found in axis\""
     ]
    }
   ],
   "source": [
    "bologna.drop('Unnamed: 0', axis=1,inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = [milano, asti, bologna,ferrara, torino, mantova, ravenna, piacenza, cesena, faenza]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 循环除去'Unnamed: 0'\n",
    "for c in cities:\n",
    "    c.drop('Unnamed: 0', axis=1,inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.98</td>\n",
       "      <td>57</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863101</td>\n",
       "      <td>0.51</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 10:38:21</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.26</td>\n",
       "      <td>51</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436866691</td>\n",
       "      <td>1.03</td>\n",
       "      <td>157.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 11:38:11</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32.36</td>\n",
       "      <td>46</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>1436870392</td>\n",
       "      <td>2.06</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 12:39:52</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.16</td>\n",
       "      <td>47</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>moderate rain</td>\n",
       "      <td>1436874000</td>\n",
       "      <td>2.06</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 13:40:00</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.48</td>\n",
       "      <td>44</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>1436877549</td>\n",
       "      <td>2.06</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>2015-07-14 14:39:09</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp  humidity  pressure    description          dt  wind_speed  wind_deg  \\\n",
       "0  29.98        57    1021.0   Sky is Clear  1436863101        0.51      90.0   \n",
       "1  30.26        51    1021.0  moderate rain  1436866691        1.03     157.0   \n",
       "2  32.36        46    1021.0   sky is clear  1436870392        2.06      67.0   \n",
       "3  31.16        47    1021.0  moderate rain  1436874000        2.06      90.0   \n",
       "4  33.48        44    1021.0   sky is clear  1436877549        2.06     135.0   \n",
       "\n",
       "      city                  day  dist  \n",
       "0  Bologna  2015-07-14 10:38:21    71  \n",
       "1  Bologna  2015-07-14 11:38:11    71  \n",
       "2  Bologna  2015-07-14 12:39:52    71  \n",
       "3  Bologna  2015-07-14 13:40:00    71  \n",
       "4  Bologna  2015-07-14 14:39:09    71  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ferrara.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各城市与海洋距离，最高温度，最低温度，最高湿度，最低湿度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 各城市与海洋距离\n",
    "dists = []\n",
    "# 最高温度\n",
    "temp_max = []\n",
    "# 最低温度\n",
    "temp_min = []\n",
    "# 最高湿度\n",
    "hum_max = []\n",
    "# 最低湿度\n",
    "hum_min = []\n",
    "\n",
    "for city in cities:\n",
    "    dists.append(city['dist'][0])\n",
    "    temp_max.append(city['temp'].max())\n",
    "    temp_min.append(city['temp'].min())  \n",
    "    hum_max.append(city['humidity'].max())\n",
    "    hum_min.append(city['humidity'].min())    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显示最高温度与离海远近的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1286b0a48>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dists, temp_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察发现，离海近的可以形成一条直线，离海远的也能形成一条直线。\n",
    "\n",
    "首先使用numpy：把列表转换为numpy数组，用于后续计算。\n",
    "\n",
    "分别以100公里和50公里为分界点，划分为离海近和离海远的两组数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(dists)\n",
    "y = np.array(temp_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[71 71  8 14 37] [33.85 33.85 32.79 32.81 32.74]\n",
      "[250 315  71  71 357 121 200] [34.81 34.31 33.85 33.85 34.69 34.18 33.92]\n"
     ]
    }
   ],
   "source": [
    "# 100公里以后的数据\n",
    "\n",
    "# x1表示小于100公里的海滨城市\n",
    "x1 = x[x < 100]\n",
    "# y1表示小于100公里城市的温度\n",
    "y1 = y[x < 100]\n",
    "print(x1, y1)\n",
    "\n",
    "x2 = x[x>50]\n",
    "y2 = y[x>50]\n",
    "print(x2, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "查看最低温度与海洋距离的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1288cfd88>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dists, temp_min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最低湿度与海洋距离的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a12894fac8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dists, hum_min)\n",
    "# 分析， 距离海越近，湿度确实高点，随着距离海越来越远，湿度会降低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最高湿度与海洋距离的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1289d4588>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dists, hum_max)\n",
    "# 距离海250公里之前，最高湿度都很高，  250公里以后， 最高湿度迅速降低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "平均湿度与海洋距离的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[26.705303030303053,\n",
       " 26.216176470588252,\n",
       " 27.242352941176495,\n",
       " 27.242352941176495,\n",
       " 26.50764705882355,\n",
       " 27.643676470588254,\n",
       " 26.94863636363639,\n",
       " 27.01852941176472,\n",
       " 26.82029411764708,\n",
       " 27.03880597014928]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_mean = []\n",
    "for city in cities:\n",
    "    temp_mean.append(city['temp'].mean())\n",
    "temp_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a128a46b88>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWgUlEQVR4nO3df2zU933H8edrxiROoXUznCh26BymwlSNJU6dlo4SKTQdLa0KQ1FCpLBkmoaUrRmZMrrQVlM3tSMLS9ZOqlqxQNstiJCCx6IqK2ULyZQ/6tQGGn4YGtamDYYGZ5MTVlkEyHt/3NfkuNzZ3wPbd/7wekgn7j6f7+fL+746Xnzvc9+7jyICMzNL16/VugAzMxtfDnozs8Q56M3MEuegNzNLnIPezCxxU2pdQDkzZsyI9vb2WpdhZjZp9Pb2vhYRLeX66jLo29vb6enpqXUZZmaThqSfV+rz1I2ZWeIc9GZmiXPQm5klzkFvZpY4B72ZWeLq8qobm7y27+ln3Y7DHBscorW5idWL5rC0o63WZZld0hz0Nma27+lnTdc+hk6fBaB/cIg1XfsAHPZmNeSpGxsz63YcPhfyw4ZOn2XdjsM1qsjMwEFvY+jY4FBV7WY2MRz0NmZam5uqajezieGgtzGzetEcmhobzmtramxg9aI5NarIzCBH0EuaKWmXpIOSDkhalbVvkbQ3u70saW+F8c2Stko6JKlP0kfG+DlYnVja0cbaZXNpa25CQFtzE2uXzfUHsWY1lueqmzPAAxGxW9J0oFfSzoi4Y3gDSY8Ar1cY/zXg+xFxm6SpwBUXXbXVraUdbQ52szozatBHxHHgeHb/pKQ+oA04CCBJwO3AwtKxkt4D3Azck41/E3hzjGo3M7Mcqpqjl9QOdADdRc0LgFcj4qUyQ64DBoBvSdoj6TFJ76qw75WSeiT1DAwMVFOWmZmNIHfQS5oGbAPuj4g3irruBDZXGDYFuBH4RkR0AL8CHiy3YUSsj4jOiOhsaSn72/lmZnYBcgW9pEYKIb8pIrqK2qcAy4AtFYYeBY5GxPA7gK0Ugt/MzCZInqtuBGwA+iLi0ZLuW4FDEXG03NiI+CXwiqTh6+s+Rja3b2ZmEyPPGf18YAWwsOhyysVZ33JKpm0ktUp6uqjpPmCTpBeBG4C/vfiyzcwsrzxX3TwPqELfPWXajgGLix7vBTovuEIzM7so/masmVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJW7UoJc0U9IuSQclHZC0KmvfUrRY+MuS9o6wjwZJeyR9bwxrNzOzHEZdHBw4AzwQEbslTQd6Je2MiDuGN5D0CPD6CPtYBfQB776oas3MrGqjntFHxPGI2J3dP0khsNuG+yUJuB3YXG68pGuBTwGPjUXBZmZWnarm6CW1Ax1Ad1HzAuDViHipwrCvAp8D3hpl3ysl9UjqGRgYqKYsMzMbQe6glzQN2AbcHxFvFHXdSeWz+U8DJyKid7T9R8T6iOiMiM6Wlpa8ZZmZ2SjyzNEjqZFCyG+KiK6i9inAMuCDFYbOBz4jaTFwOfBuSY9HxF0XV7aZmeWV56obARuAvoh4tKT7VuBQRBwtNzYi1kTEtRHRDiwHnnHIm5lNrDxTN/OBFcDCosspF2d9yymZtpHUKunpMa7TzMwu0KhTNxHxPKAKffeUaTsGLC7T/izwbLUFmpnZxfE3Y83MEuegNzNLnIPezCxxDnozs8Q56M3MEuegNzNLnIPezCxxDnozs8Q56M3MEuegNzNLnIPezCxxDnozs8Q56M3MEpdr4RGrrS9u38fm7lc4G0GDxJ0fnsmXl86tdVlmNkk46OvcF7fv4/Ef/uLc47MR5x477M0sD0/d1LnN3a9U1W5mVspBX+fORlTVbmZWykFf5xpUdnGviu1mZqUc9HXuzg/PrKrdzKzUqEEvaaakXZIOSjogaVXWvqVosfCXJe3NO9by+/LSudw1733nzuAbJO6a9z5/EGtmuSlGmeuVdA1wTUTsljQd6AWWRsTBom0eAV6PiL+pdmw5nZ2d0dPTc2HPyMzsEiSpNyI6y/WNekYfEccjYnd2/yTQB7QV7VzA7cDmaseamdn4q2qOXlI70AF0FzUvAF6NiJcuYGxx/0pJPZJ6BgYGqinLzMxGkDvoJU0DtgH3R8QbRV13UuZsPufYcyJifUR0RkRnS0tL3rLMzGwUub4ZK6mRQlBvioiuovYpwDLgg9WONTOziZHnqhsBG4C+iHi0pPtW4FBEHL2AsWZmNgHyTN3MB1YAC4sup1yc9S2nZNpGUqukp3OMNTOzCTDq1E1EPA+U/RpmRNxTpu0YsHi0sWZmNjH8zVgzs8Q56M3MEuegNzNLnIPezCxxDnozs8Q56M3MEuegNzNLnIPezCxxDnozs8Q56M3MEuegNzNLnIPezCxxDnozs8TlWnjECrbv6WfdjsMcGxyitbmJ1YvmsLTDS+CaWX1z0Oe0fU8/a7r2MXT6LAD9g0Os6doH4LA3s7rmqZuc1u04fC7khw2dPsu6HYdrVJGZWT4+o8/p2OBQVe1mwzzlZ7XmM/qcWpubqmo3g7en/PoHhwjenvLbvqe/1qXZJSTP4uAzJe2SdFDSAUmrsvYtRevAvixpb4Xxn5B0WNIRSQ+Ocf0TZvWiOTQ1NpzX1tTYwOpFc2pUkU0GnvKzepBn6uYM8EBE7JY0HeiVtDMi7hjeQNIjwOulAyU1AF8HPg4cBX4k6amIODg25U+c4bfafgtu1fCUn9WDPIuDHweOZ/dPSuoD2oCDAJIE3A4sLDP8Q8CRiPhptu0TwJLhsZPN0o42B7tVpbW5if4yoe4pP5tIVc3RS2oHOoDuouYFwKsR8VKZIW3AK0WPj2Zt5fa9UlKPpJ6BgYFqyjKrW57ys3qQO+glTQO2AfdHxBtFXXcCmy+2kIhYHxGdEdHZ0tJysbszqwtLO9pYu2wubc1NCGhrbmLtsrl+Z2gTKtfllZIaKYT8pojoKmqfAiwDPlhhaD8ws+jxtVmb2SXDU35Wa3muuhGwAeiLiEdLum8FDkXE0QrDfwS8X9J1kqYCy4GnLqZgMzOrTp4z+vnACmBf0SWUn4+IpykE93nTNpJagcciYnFEnJH0WWAH0ABsjIgDY1Z9EX8pxcysPEVErWt4h87Ozujp6cm9fenv0EDhAy/PhZrZpUJSb0R0lutL4pux/lKKmVllSQS9v5RiZlZZEkHv36ExM6ssiaD3l1LMzCpL4meK/Ts0ZmaVJRH04C+lmJlVksTUjZmZVeagNzNLnIPezCxxDnozs8Q56M3MEuegNzNLnIPezCxxDnozs8Q56M3MEuegNzNLXDI/gVCOV50yM0s46EtXneofHGJN1z4Ah72ZXVKSnbrxqlNmZgWjBr2kmZJ2SToo6YCkVUV990k6lLU/XGH8n2f9+yVtlnT5WD6BSrzqlJlZQZ6pmzPAAxGxW9J0oFfSTuBqYAlwfUScknRV6UBJbcCfAR+IiCFJTwLLgW+P2TOooLW5if4yoe5Vp8zsUjPqGX1EHI+I3dn9k0Af0AbcCzwUEaeyvhMVdjEFaJI0BbgCODYWhY/Gq06ZmRVUNUcvqR3oALqB2cACSd2SnpN0U+n2EdEP/D3wC+A48HpE/KDCvldK6pHUMzAwUOXTeKelHW2sXTaXtuYmBLQ1N7F22Vx/EGtmlxxFRL4NpWnAc8BXIqJL0n5gF4WpmZuALcCsKNqhpPcC24A7gEHgu8DWiHh8pL+rs7Mzenp6qn82ZmaXKEm9EdFZri/XGb2kRgqBvSkiurLmo0BXFLwAvAXMKBl6K/CziBiIiNNAF/C7F/IkzMzswuS56kbABqAvIh4t6toO3JJtMxuYCrxWMvwXwDxJV2T7+RiFOX4zM5sgec7o5wMrgIWS9ma3xcBGYFY2hfMEcHdEhKRWSU8DREQ3sBXYDezL/r714/FEzMysvNxz9BPJc/RmZtW56Dl6MzObvBz0ZmaJc9CbmSXOQW9mlrhkf6bYzC6M13FIj4PezM7xOg5p8tSNmZ3jdRzS5KA3s3O8jkOaHPRmdk6l9Rq8jsPk5qA3s3O8jkOa/GGsmZ0z/IGrr7pJi4PezM6ztKPNwZ4YT92YmSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4kYNekkzJe2SdFDSAUmrivruk3Qoa3+4wvhmSVuz7fokfWQsn4CZmY0szxemzgAPRMRuSdOBXkk7gauBJcD1EXFK0lUVxn8N+H5E3CZpKnDFmFRuZma5jBr0EXEcOJ7dPympD2gD/hh4KCJOZX0nSsdKeg9wM3BPts2bwJtjVbyZmY2uqjl6Se1AB9ANzAYWSOqW9Jykm8oMuQ4YAL4laY+kxyS9q8K+V0rqkdQzMDBQ3bMwM7OKcge9pGnANuD+iHiDwruBK4F5wGrgSUkqGTYFuBH4RkR0AL8CHiy3/4hYHxGdEdHZ0tJS/TMxM7OycgW9pEYKIb8pIrqy5qNAVxS8ALwFzCgZehQ4GhHd2eOtFILfzMwmSJ6rbgRsAPoi4tGiru3ALdk2s4GpwGvFYyPil8ArkoZ/zPpjwMGLL9vMzPLKc9XNfGAFsE/S3qzt88BGYKOk/RQ+YL07IkJSK/BYRCzOtr0P2JRdcfNT4A/H8gmYmdnI8lx18zxQOvc+7K4y2x8DFhc93gt0XmB9ZmZ2kfzNWDOzxDnozcwS56A3M0ucg97MLHFeHNzMrMa27+ln3Y7DHBscorW5idWL5ozpAu0OejOzGtq+p581XfsYOn0WgP7BIdZ07QMYs7D31I2ZWQ2t23H4XMgPGzp9lnU7Do/Z3+GgNzOroWODQ1W1XwgHvZlZDbU2N1XVfiEc9GZmNbR60RyaGhvOa2tqbGD1ojkVRlTPH8aamdXQ8AeuvurGzCxhSzvaxjTYS3nqxswscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PE5VkcfKakXZIOSjogaVVR332SDmXtD4+wjwZJeyR9b6wKNzOzfPJcR38GeCAidkuaDvRK2glcDSwBro+IU5KuGmEfq4A+4N0XXbGZmVVl1DP6iDgeEbuz+ycpBHYbcC/wUEScyvpOlBsv6VrgU8BjY1W0mZnlV9UcvaR2oAPoBmYDCyR1S3pO0k0Vhn0V+Bzw1ij7XimpR1LPwMBANWWZmdkIcge9pGnANuD+iHiDwrTPlcA8YDXwpCSVjPk0cCIiekfbf0Ssj4jOiOhsaWmp5jmYmdkIcgW9pEYKIb8pIrqy5qNAVxS8QOGMfUbJ0PnAZyS9DDwBLJT0+JhUbmZmueS56kbABqAvIh4t6toO3JJtMxuYCrxWPDYi1kTEtRHRDiwHnomIu8amdDMzyyPPGf18YAWFs/G92W0xsBGYJWk/hbP1uyMiJLVKenocazYzsyqMenllRDwPqEL3O87OI+IYsLhM+7PAs9WVZ2ZmF8vfjDUzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS9yoK0yZmaVm+55+1u04zLHBIVqbm1i9aA5LO9pqXda4cdCb2SVl+55+1nTtY+j0WQD6B4dY07UPINmwH3XqRtJMSbskHZR0QNKqor77JB3K2h+uZqyZWS2s23H4XMgPGzp9lnU7DteoovGX54z+DPBAROyWNB3olbQTuBpYAlwfEackXZV3bEQcHLNnYGZWhWODQ1W1p2DUM/qIOB4Ru7P7J4E+oA24F3goIk5lfSeqGGtmVhOtzU1VtaegqqtuJLUDHUA3MBtYIKlb0nOSbqpibLn+lZJ6JPUMDAxUU5aZWW6rF82hqbHhvLamxgZWL5pTo4rGX+6glzQN2AbcHxFvUJj2uRKYB6wGnpSknGPfISLWR0RnRHS2tLRU+TTMzPJZ2tHG2mVzaWtuQkBbcxNrl81N9oNYyHnVjaRGCkG9KSK6suajQFdEBPCCpLeAGcBAjrFmZjWztKMt6WAvleeqGwEbgL6IeLSoaztwS7bNbGAq8FrOsWZmNkHyTN3MB1YACyXtzW6LgY3ALEn7gSeAuyMiJLVKenqUsWZmNkFGnbqJiOeBsnPvwF1ltj8GLM4x1szMJoB/68bMLHEOejOzxKlw0Ux9kTQA/HyETWZQ8sFvHZtMtYLrHU+TqVaYXPVOplphfOr9jYgoe216XQb9aCT1RERnrevIYzLVCq53PE2mWmFy1TuZaoWJr9dTN2ZmiXPQm5klbrIG/fpaF1CFyVQruN7xNJlqhclV72SqFSa43kk5R29mZvlN1jN6MzPLyUFvZpa4SRX0kj4h6bCkI5IerHU95Uh6WdK+7Hd9erK2KyXtlPRS9ud7a1jfRkknst8oGm4rW58K/jE73i9KurEOav2SpP5yv50kaU1W62FJiyay1uzvL7t0Zj0e3xFqrcvjK+lySS9I+nFW719n7ddla2IckbRF0tSs/bLs8ZGsv70Oav22pJ8VHdsbsvbxfx1ExKS4AQ3AfwOzKPxS5o+BD9S6rjJ1vgzMKGl7GHgwu/8g8Hc1rO9m4EZg/2j1UfjNon+n8HtF84DuOqj1S8BflNn2A9lr4jLguuy10jDB9V4D3Jjdnw78JKur7o7vCLXW5fHNjtG07H4jhQWM5gFPAsuz9m8C92b3/wT4ZnZ/ObClDmr9NnBbme3H/XUwmc7oPwQciYifRsSbFH4xc0mNa8prCfCd7P53gKW1KiQi/gv435LmSvUtAf45Cn4INEu6ZkIKpWKtlSwBnoiIUxHxM+AIhdfMhInKS2fW3fEdodZKanp8s2P0f9nDxuwWwEJga9ZeemyHj/lW4GNS+YWRJrDWSsb9dTCZgr4NeKXo8VHqc/3ZAH4gqVfSyqzt6og4nt3/JYWF1etJpfrq9Zh/NnuLu7FoGqyuatX5S2fW9fHVO5f5rMvjK6lB0l7gBLCTwruKwYg4U6amc/Vm/a8Dv16rWiNi+Nh+JTu2/yDpstJaM2N+bCdT0E8WH42IG4FPAn8q6ebizii8V6vba1rrvT7gG8BvAjcAx4FHalpNGRph6cx6O75laq3b4xsRZyPiBuBaCu8mfqu2FVVWWquk3wbWUKj5JgrLsP7lRNUzmYK+H5hZ9PjarK2uRER/9ucJ4F8pvCBfHX4rlv15onYVllWpvro75hHxavaP6C3gn3h7+qAualX5pTPr8viWq7Xejy9ARAwCu4CPUJjmGF5Xo7imc/Vm/e8B/mdiKz2v1k9k02UREaeAbzGBx3YyBf2PgPdnn7JPpfABy1M1ruk8kt4lafrwfeD3gP0U6rw72+xu4N9qU2FFlep7CviD7KqAecDrRVMQNVEyd/n7FI4vFGpdnl1tcR3wfuCFCa6t0tKZdXd8K9Var8dXUouk5ux+E/BxCp8r7AJuyzYrPbbDx/w24Jns3VStaj1U9J+9KHyWUHxsx/d1MNaf7o7njcKn0z+hMDf3hVrXU6a+WRSuTPgxcGC4Rgpzg/8JvAT8B3BlDWvcTOEt+WkKc4F/VKk+ClcBfD073vuAzjqo9V+yWl7M/oFcU7T9F7JaDwOfrMGx/SiFaZkXgb3ZbXE9Ht8Raq3L4wv8DrAnq2s/8FdZ+ywK/+EcAb4LXJa1X549PpL1z6qDWp/Jju1+4HHevjJn3F8H/gkEM7PETaapGzMzuwAOejOzxDnozcwS56A3M0ucg97MLHEOejOzxDnozcwS9/+ANvdQs+G+ywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dists, temp_mean)\n",
    "# 分析 150公里之前，平均湿度逐渐升高，150公里时达到最高，150之后，平均湿度逐渐降低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "风向与风速的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a129a96908>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WqVn1NqlWqVn1NqlWqVn1TqtWLn0AQHAENQAEFzGo1+ouYAJNqlVqVr1NqlVqVr1NqlVqVr1TqTXcNWoAwG4RP1EDAHYgqAEguDBBbWZ3mdk3zOw5M3t/3fUMY2YbZvY1M3vCzNazttea2ZfM7FvZ80/UVNvHzOyCmT29o21obZb8RdbXT5nZbUHq/aCZPZ/17xNmdveOdR/I6v2Gmf1yxbXebGaPmtk5M3vGzN6TtYfr3zG1Ru3bV5rZ42b2ZFbvh7L215vZY1ldnzSzV2Tt12bLz2XrewFq/biZfXtH396atZc3Dty99oeklqR/k3SLpFdIelLS8brrGlLnhqTr97T9iaT3Z6/fL+mPa6rtbZJuk/R0Xm2S7pb0d5JM0u2SHgtS7wcl/f6QbY9nY+JaSa/PxkqrwlpvkHRb9vo1kr6Z1RSuf8fUGrVvTdKrs9fXSHos67NPSbo3a/+opOXs9Yqkj2av75X0yQC1flzSO4dsX9o4iPKJ+s2SnnP3f3f3/5X0sKR7aq6pqHskPZS9fkjSr9ZRhLt/RdJ39zSPqu0eSX/tyT9Lus7Mbqik0MyIeke5R9LD7v4jd/+2pOeUxkwl3P1Fd//X7PX/SHpW0jEF7N8xtY5Sd9+6u/8wW7wme7ikOyR9Omvf27dbff5pSafMzGqudZTSxkGUoD4m6T92LP+nxg+uurikvzezs2a2lLW9zt1fzF7/l6TX1VPaUKNqi9zfv5P9mvixHZeRwtSb/ar9JqVPU6H7d0+tUtC+NbOWmT0h6YKkLyl9qv+eu18eUtPL9Wbrvy/pJ+uq1d23+nY169s/M7Nr99aaOXDfRgnqpniru98m6e2S3m1mb9u50tPvOyG/7xi5th0ekPRTkm6V9KKkP621mj3M7NWSPiPpve7+g53rovXvkFrD9q27X3H3WyXdpPRp/mfqrWi0vbWa2RslfUCp5p+X9FpJ7yt7v1GC+nlJN+9YvilrC8Xdn8+eL0j6nNKgemnr15ns+UJ9Fe4zqraQ/e3uL2V/EK5K+ktt/wpee71mdo1S8A3c/bNZc8j+HVZr5L7d4u7fk/SopF9QukywNVXgzpperjdb/+OS/rvaSnfVeld2ucnd/UeS/kpT6NsoQf0vkt6Q3el9hdJNgi/UXNMuZvZjZvaardeSfknS00p1vivb7F2SPl9PhUONqu0Lkn4ruyt9u6Tv7/gVvjZ7rt/9mlL/Sqnee7M7/q+X9AZJj1dYl0l6UNKz7v7hHavC9e+oWgP37byZXZe9fpWkO5Wuqz8q6Z3ZZnv7dqvP3ynpkey3mbpq/fqOv6xN6Vr6zr4tZxxUdcc076F0h/SbSten7qu7niH13aJ0d/xJSc9s1ah0fewfJX1L0j9Iem1N9X1C6Vfa/1O6Fvbbo2pTugv9kayvvyapH6Tev8nqeSob5Dfs2P6+rN5vSHp7xbW+VemyxlOSnsged0fs3zG1Ru3bn5X01ayupyX9YdZ+i9JfGM9J+ltJ12btr8yWn8vW3xKg1keyvn1a0hltfzOktHHAPyEHgOCiXPoAAIxAUANAcAQ1AARHUANAcAQ1AARHUANAcAQ1AAT3/33uc/GkwFxaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# milano风向与风速的关系\n",
    "plt.plot(milano['wind_deg'], milano['wind_speed'], 'ro')\n",
    "# 竖轴代表风速，横轴代表风向"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在子图中，同时比较风向与湿度和风力的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a129bd0848>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 湿度与风向的关系\n",
    "axes1 = plt.subplot(121)\n",
    "axes1.scatter(milano['wind_deg'], milano['humidity'])\n",
    "\n",
    "# 风速与风向的关系\n",
    "axes1 = plt.subplot(122)\n",
    "axes1.scatter(milano['wind_deg'], milano['wind_speed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到散点图显示效果不好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于风向是360度，我们可以考虑使用玫瑰图（极坐标条形图）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先自定义一个画图函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_rose(values, title):\n",
    "    # 玫瑰图花瓣的个数8, 45度    \n",
    "    n = 8\n",
    "    angle = np.arange(0, 2*np.pi, 2 * np.pi/n)\n",
    "    \n",
    "    # 绘制数据的values    \n",
    "    radius = np.array(values)\n",
    "    \n",
    "    # axis: 轴\n",
    "    # axes：整个画面\n",
    "    plt.axes([0,0,2,2], polar=True)\n",
    "    \n",
    "    color = np.random.random(size=24).reshape((8, 3))\n",
    "    \n",
    "    plt.bar(angle, radius, color=color)\n",
    "    \n",
    "    plt.title(title, loc='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用numpy创建一个直方图，将360度划分为8个面元，将数据分类到这8个面元中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3, 1, 2, 2], dtype=int64), array([1.  , 1.75, 2.5 , 3.25, 4.  ]))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1,1.2,1,2,3,3.6,3,4])\n",
    "np.histogram(x, 4, [1, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree = milano['wind_deg']\n",
    "d, b = np.histogram(degree, 8, [0, 360])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_rose(d, 'milano')\n",
    "# 分析，观察图可直观的发现，0°风最强"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
